Stock Price Prediction Using a Stacked Heterogeneous Ensemble

Michael Parker, Mani Ghahremani () and Stavros Shiaeles
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Michael Parker: School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK
Mani Ghahremani: School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK
Stavros Shiaeles: School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK

IJFS, 2025, vol. 13, issue 4, 1-23

Abstract: Forecasting stock price ranges remains a significant challenge because of the non-linear nature of financial data. This study proposes and evaluates a stacking ensemble model for range-based volatility forecasting, using open, high, low, and close (OHLC) prices. The model integrates a diverse, heterogeneous set of base learners, such as statistical (ARIMA), machine learning (Random Forest), and deep learning (LSTM, GRU, Transformer) models, with an XGBoost meta-learner. Applied to several major financial indices and a single stock, the proposed framework demonstrates high predictive accuracy, achieving R 2 scores between 0.9735 and 0.9905. These results highlight the efficacy of a multi-faceted stacking approach in navigating the complexities of financial forecasting.

Keywords: stacked machine learning; stock price forecasting; LSTM; ARIMA; XGBoost; GRU; transformer (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2025
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